TDWI Certificate Track
Geared to both technical and non-technical personnel, TDWI’s Data Science Bootcamp provides a comprehensive overview of every facet of a data science program, providing principles and practices for business stakeholders and data scientists to drive business value from data through machine learning and AI.
This three-day workshop uses a process-oriented framework to introduce the discipline of data science, placing activities in the context of business value and covering the key concepts that every data scientist and business stakeholder needs to know. Key principles are described for each stage of a data science initiative, and real-world examples illustrate the concepts.
The bootcamp begins with a complete overview of data science programs, roles, and processes, and teaches students how projects are defined so that analytics goals have clear links to business challenges. Students will then learn about data selection and data preparation—crucial activities that are incorporated into multiple phases of a data science project and are repeated as part of an iterative process as models are designed. Next, students will learn how analytic models are bult, validated, and deployed. They will receive an overview of common statistical techniques and machine learning and AI algorithms used in analytic models, including what they are and how they find patterns—without an in-depth treatment of the mathematics. Plus, they will learn visualization and storytelling techniques for communicating analytics insights to others.
Modern analytics programs require an approach that is repeatable, agile, and scalable. During the final day of the data science bootcamp, students will learn DataOps and MLOps methods for delivering data and analytics to meet this challenge, providing a mechanism for rapid and repeatable delivery of data and analytics that is scalable and manageable, while also living up to business expectations.
Prepare your team to unlock powerful business insights through data science with the TDWI Data Science Bootcamp.
Your Team Will Learn
- How data science programs are organized and key roles such as business stakeholders, subject matter experts, data engineers, and analytic modelers
- The purpose of machine learning, deep learning, and AI, and how they are matched to business challenges
- The major stages of a data science project, including establishment of goals, data preparation, analytic modeling, and deployment
- The importance of assessing data quality when choosing data sources
- Data preparation activities such as cleansing, integration, and feature engineering
- How analytics models are trained, tuned, and validated
- Common analytics techniques such as classification, clustering, association, sequencing, and more
- Statistical methods such as linear regression and their role in data science
- Common algorithms such as k-means and neural networks, and how they are used in data science
- Best practices for applying visualization in each stage of a data science project
- Exploratory data analysis (EDA) techniques that support problem framing and source selection
- How to communicate data science insights to technical and non-technical stakeholders
- Definition, scope, and components of DataOps and MLOps
- How these approaches apply agile and DevOps principles to data and analytics
- How these approaches reduce technical debt
- The central role of automation in DataOps and MLOps
- Technologies that support every stage of the data science process
- Best practices and how to get started
- This workshop is geared to technical and non-technical professionals getting started with data science, including:
- Business analysts
- Business stakeholders
- Data scientists
- Analytics practitioners
- Data engineers
- Analytics project leads
- BI and data management professionals